Methods and systems for refreshing current page information

ABSTRACT

The present disclosure discloses a method and system for refreshing current page information. The method may include: obtaining currently displayed information data in response to receiving an information refresh request from a user; determining possibly-interested information of the user based on the information refresh request and the currently-displayed information data; displaying the possibly-interested information of the user. The present disclosure may determine the possibly-interested information of the user based on the currently-displayed information data, and recommend data content of a different type from the currently-displayed information data to the user, which may broaden a browsing horizon of the user, and improve user experience.

TECHNICAL FIELD

The present disclosure relates to the field of content recommendation,in particular, to methods and systems for refreshing current pageinformation.

BACKGROUND

With the rapid development of Internet technology, when users open awebsite of video, news, music, or shopping, etc., they often receivecontents recommended by the website. However, users sometimes may not beinterested in the contents recommended by the website, but want to learnmore about different types of new information. Therefore, it isdesirable to provide methods and systems for refreshing current pageinformation.

SUMMARY

One of the embodiments of the present disclosure may provide a methodfor refreshing current page information. The method may include: inresponse to receiving an information fresh request from a user,obtaining currently-displayed information data; determiningpossibly-interested information of the user based on the informationrefresh request and the currently-displayed information data; displayingthe possibly-interested information of the user to the user.

In some embodiments, the information refresh request from the user maybe made by a coded gesture, a continuous click operation, a key clickoperation, a touch screen operation in a pause state, a continuousshaking operation, a voice input operation, a face recognition, a facialexpression recognition, or an iris recognition.

In some embodiments, the information refresh request may be areverse-information refresh request.

In some embodiments, the information refresh request may include areversal threshold. The reversal threshold may be used to characterize adegree of association between refreshed information and thecurrently-displayed information data.

In some embodiments, a category of the reversal threshold may at leastinclude a reversal of a same type, a reversal of different types, or areversal of a minority type.

In some embodiments, determining possibly-interested information of theuser based on the information refresh request and thecurrently-displayed information data may include: determining a type ofthe currently-displayed information data based on thecurrently-displayed information data; obtaining a type of thepossibly-interested information of the user by performing, based on theinformation refresh request and the type of the currently-displayedinformation data, a reversal operation on the type of thecurrently-displayed information data; determining thepossibly-interested information of the user based on the type of thepossibly-interested information of the user.

In some embodiments, determining a type of the currently-displayedinformation data based on the currently-displayed information data mayinclude: determining the type of the currently-displayed informationdata by processing the currently-displayed information data using amachine learning model.

In some embodiments, a machine learning model may include aclassification model. The machine learning model may be obtainedaccording to a process including: obtaining a training sample; thetraining samples may include historical displayed information data and atype of the historical displayed information data, the type of thehistorical displayed information data may be labelled as a type ofreference information data; obtaining the machine by training apreliminary model based on the training sample.

In some embodiments, obtaining a type of the possibly-interestedinformation of the user by performing, based on the information refreshrequest and the type of the currently-displayed information data,according to a reversal operation on the type of the currently-displayedinformation data may include: selecting, based on the type of thecurrently-displayed information data, at least one data type from acategory of the reversal threshold as the type of thepossibly-interested information of the user.

In some embodiments, determining possibly-interested information of theuser based on the information refresh request and thecurrently-displayed information data may include: in response toreceiving the information refresh request from the user, obtaininghistorical browsing information of the user; determining a type ofinformation data that the user is not interested in based on thecurrently-displayed information data and the historical browsinginformation of the user; obtaining a type of the possibly-interestedinformation of the user by performing, based on the information refreshrequest and the type of information data that the user is not interestedin, a reversal operation on the type of the possibly-interestedinformation of the user; determining the possibly-interested informationof the user based on the type of the possibly-interested information ofthe user.

In some embodiments, displaying the possibly-interested information ofthe user to the user may include: displaying at least part of thepossibly-interested information of the user on a terminal of the useraccording to a feed stream.

One of the embodiments of the present disclosure may provide a systemfor refreshing current page information. The system may include at leastone memory configured to store computer instructions, and at least oneprocessor in communication with the at least one memory. When the atleast one processor executes the computer instructions, the at least oneprocessor may cause the system to: in response to receiving aninformation refresh request from a user, obtain currently-displayedinformation data; determine possibly-interested information of the userbased on the information refresh request and the currently-displayedinformation data; display the possibly-interested information of theuser to the user.

In some embodiments, the information refresh request may be areverse-information refresh request.

In some embodiments, to determine possibly-interested information of theuser based on the information refresh request and thecurrently-displayed information data, the at least one processor mayfurther cause the system to: determine a type of the currently-displayedinformation data based on the currently-displayed information data;obtain a type of the possibly-interested information of the user byperforming, based on the information refresh request and the type of thecurrently-displayed information data, a reversal operation on the typeof the currently-displayed information data; determine thepossibly-interested information of the user based on the type of thepossibly-interested information of the user.

In some embodiments, to determine a type of the currently-displayedinformation data based on the currently-displayed information data, theat least one processor may further cause the system to: determine thetype of the currently-displayed information data by processing thecurrently-displayed information data using a machine learning model.

In some embodiments, to obtain a type of the possibly-interestedinformation of the user by performing, based on the type of thecurrently-displayed information data, a reversal operation on the typeof the currently-displayed information data, the at least one processormay further cause the system to: select, based on thecurrently-displayed information data, at least one data type from acategory of the reversal threshold as the type of thepossibly-interested information of the user.

In some embodiments, to determine possibly-interested information of theuser based on the information refresh request and thecurrently-displayed information data, the at least one processor mayfurther cause the system to: in response to receiving the informationrefresh request from the user, obtain historical browsing information ofthe user; determine a type of information data that the user is notinterested in based on the currently-displayed information data and thehistorical browsing information of the user; obtain a type of thepossibly-interested information of the user by performing, based on theinformation refresh request and the type of information data that theuser is not interested in, a reversal operation on the type of theinformation data that the user is not interested in; determine thepossibly-interested information of the user based on the type of thepossibly-interested information of the user.

In some embodiments, to display the possibly-interested information ofthe user to the user, the at least one processor may further cause thesystem to: display at least part of the possibly-interested informationof the user on a terminal of the user according to a feed stream.

One of the embodiments of the present disclosure may provide a systemfor refreshing current page information. The system may include: anobtaining module configured to obtain currently-displayed informationdata in response to receiving an information refresh request from auser; a determining module configured to determine possibly-interestedinformation of the user based on the information refresh request and thecurrently-displayed information data; a display module configured todisplay the possibly-interested information of the user to the user.

According to another aspect of the present disclosure, it may relate toa computer-readable storage medium. The storage medium may storecomputer instructions. When a computer reads the computer instructionsin the storage medium, the computer may execute the methods described inany embodiment of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure may further be illustrated in terms of exemplaryembodiments. These exemplary embodiments may be described in detail withreference to the drawings. The embodiments may not be restrictive. Inthe embodiments, the same number may represent the same structure,wherein:

FIG. 1 is a diagram illustrating an application scenario of a currentpage information refresh system according to some embodiments of thepresent disclosure;

FIG. 2 is a block diagram of a current page information refresh systemaccording to some embodiments of the present disclosure;

FIG. 3 is an exemplary flowchart of a process for refreshing currentpage information according to some embodiments of the presentdisclosure;

FIG. 4 is an exemplary flowchart of a process for determiningpossibly-interested information of a user according to some embodimentsof the present disclosure;

FIG. 5 is an exemplary flowchart of a process for determiningpossibly-interested information of a user according to some embodimentsof the present disclosure; and

FIG. 6 is an exemplary flowchart of a process for training a machinelearning model according to some embodiments of the present disclosure.

DETAILED DESCRIPTION

To describe the technical solutions of the embodiments of the presentdisclosure more clearly, the following may briefly introduce thedrawings for the description of the embodiments. Obviously, the drawingsin the following description may be only some examples or embodiments ofthe present disclosure. For those skilled in the art, without creativework, the present disclosure may be applied to other similar scenariosbased on the drawings. Unless obviously obtained from the context or thecontext is illustrated otherwise, the same number in the drawings mayrefer to the same structure or operation.

It should be understood that the “system,” “device,” “unit,” and/or“module” used herein may be a way to distinguish different components,elements, units, parts, or assemblies of different levels. However, ifother words may achieve the same purpose, the words may be replaced byother expressions.

As used in the disclosure and the appended claims, the singular forms“a,” “an,” and “the” may include plural referents unless the content isclearly dictated otherwise. Generally speaking, the terms “comprising”and “including” only suggest that the steps and elements that have beenclearly identified may be included, the steps and elements do notconstitute an exclusive list, and the method or device may also includeother steps or elements.

In the present disclosure, a flowchart may be used to illustrate theoperations performed by the system according to the embodiments of thepresent disclosure. It should be understood that the preceding orfollowing operations may not be performed in order precisely. Instead,the individual step may be processed in reverse order, or at the sametime. At the same time, other operations may also be added to theseprocesses, or a step or several operations may be removed from theseprocesses.

FIG. 1 is a diagram illustrating an application scenario of a currentpage information refresh system according to some embodiments of thepresent disclosure.

A current page information refresh system 100 may refresh displayedinformation of a current page according to the needs of a user, so thatthe user may learn more about other types of information. The currentpage information refresh system 100 may be a service platform for theInternet or other networks. For example, the current page informationrefresh system 100 may be an online service platform that provides theuser with information or video information. In some embodiments, thecurrent page information refresh system 100 may be applied to an onlineshopping service, such as buying clothes, books, daily necessities, orthe like. In some embodiments, the current page information refreshsystem 100 may also be applied to the field of travel (e.g., tourism)services. The current page information refresh system 100 may include,but is not limited to, a server 110, a user terminal 120, a storagedevice 130, an information source 140, and a network 150.

In some embodiments, the server 110 may be configured to processinformation and/or data related to a service request, for example, toprocess an information refresh request from a user. Specifically, theserver 110 may receive the information refresh request from the userterminal 120, and process the information refresh request and sendpossibly-interested information of the user to the user terminal 120. Insome embodiments, the server 110 may be a single server or a servergroup. The server group may be centralized or distributed (e.g., theserver 110 may be a distributed system). In some embodiments, the server110 may be local or remote. For example, the server 110 may accessinformation and/or data stored in the storage device 130 and the userterminal 120 via the network 150. As another example, the server 110 maybe directly connected to the storage device 130 and the user terminal120 to access the stored information and/or data. In some embodiments,the server 110 may be implemented on a cloud platform. Merely by way ofexample, the cloud platform may include a private cloud, a public cloud,a hybrid cloud, a community cloud, a distributed cloud, an inter-cloud,a multiple cloud, or the like, or any combination thereof.

In some embodiments, the server 110 may include a processing engine 112.The processing engine 112 may process data and/or information related toa current page information refresh request to perform one or morefunctions described in the present disclosure. For example, theprocessing engine 112 may receive an information refresh request sent bythe user terminal 120, obtain currently-displayed information data,determine possibly-interested information of the user based on theinformation refresh request and the currently-displayed informationdata, and finally display the possibly-interested information of theuser. In some embodiments, the processing engine 112 may include one ormore processing engines (e.g., a single-chip processing engine or amulti-chip processor). Merely by way of example, the processing engine112 may include a central processing unit (CPU), an application-specificintegrated circuit (ASIC), an application-specific instruction-setprocessor (ASIP), an image processing unit (GPU), a physical operationprocessing unit (PPU), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic device (PLD), acontroller, a microcontroller unit, a reduced instruction-set computer(RISC), a micro-processor, or the like, or any combination thereof.

In some embodiments, the user terminal 120 may be a person, a tool, orother entities directly related to an information refresh request. Auser may be an information refresh requester. In the present disclosure,“user” and “user terminal” may be used interchangeably. In someembodiments, the user terminal 120 may include a mobile device 120-1, atablet computer 120-2, a laptop computer 120-3, a desktop computer120-4, or the like, or any combination thereof. In some embodiments, themobile device 120-1 may include a smart home device, a wearable device,a smart mobile device, a virtual reality device, an augmented realitydevice, or the like, or any combination thereof. In some embodiments, asmart home device may include a smart lighting device, a smartelectrical appliance control device, a smart monitoring device, a smartTV, a smart camera, a walkie-talkie, or the like, or any combinationthereof. In some embodiments, a wearable device may include a smartbracelet, smart footwear, smart glasses, a smart helmet, a smartwatch, asmart wearer, a smart backpack, smart accessories, or the like, or anycombination thereof. In some embodiments, a smart mobile device mayinclude a smartphone, a personal digital assistant (PDA), a gamingdevice, a navigation device, a point of sale (POS), or the like, or anycombination thereof. In some embodiments, a virtual reality deviceand/or an augmented reality device may include a virtual reality helmet,virtual reality glasses, virtual reality goggles, an augmented virtualreality helmet, augmented reality glasses, augmented reality goggles, orthe like, or any combination thereof. For example, the virtual realitydevice and/or augmented reality device may include Google Glass, OculusRift, HoloLens, Gear VR, or the like.

The storage device 130 may store data and/or instructions related to aninformation refresh request from a user. In some embodiments, thestorage device 130 may store currently-displayed information data. Insome embodiments, the storage device 130 may store historical browsinginformation of the user. In some embodiments, the storage device 130 maystore data and/or instructions used by the server 110 to execute orcomplete the exemplary methods described in the present disclosure. Insome embodiments, the storage device 130 may include a mass memory, aremovable memory, a volatile read-write memory, a read-only memory(ROM), or the like, or any combination thereof. Exemplary mass storagedevices may include a magnetic disk, an optical disk, a solid-statedisk, or the like. Exemplary removable storages may include a flashdrive, a floppy disk, an optical disk, a memory card, a compact disk, amagnetic tape, or the like. Exemplary volatile read-only memories mayinclude a random-access memory (RAM). Exemplary RAMs may include adynamic RAM (DRAM), a double rate synchronous dynamic RAM (DDR SDRAM), astatic RAM (SRAM), a thyristor RAM (T-RAM), a zero capacitance RAM(Z-RAM), or the like. Exemplary ROMs may include a mask ROM (MROM), aprogrammable ROM (PROM), an erasable programmable ROM (PEROM), anelectronically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), a digital general-purpose disk ROM, or the like. In someembodiments, the storage device 130 may be implemented on a cloudplatform. Merely by way of example, the cloud platform may include aprivate cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an internal cloud, a multi-layer cloud, or the like,or any combination thereof.

In some embodiments, the storage device 130 may be connected to thenetwork 150 to communicate with one or more components (e.g., the server110, the user terminal 120) in the current page information refreshsystem 100. The one or more components in the current page informationrefresh system 100 may access data or instructions stored in the storagedevice 130 through the network 150. In some embodiments, the storagedevice 130 may directly connect to or communicate with the one or morecomponents (e.g., the server 110, the user terminal 120) of the currentpage information refresh system 100. In some embodiments, the storagedevice 130 may be part of the server 110.

The network 150 may facilitate the exchange of information and/or data.In some embodiments, one or more components (e.g., the server 110, theuser terminal 120, and the storage device 130) in the current pageinformation refresh system 100 may send information and/or datato/receive information and/or data from other components of the currentpage information refresh system 100 via the network 150. For example,the server 110 may obtain/acquire a service request (e.g., aninformation refresh request) from the user terminal 120 through thenetwork 150. In some embodiments, the network 150 may be any form ofwired or wireless networks, or any combination thereof. Merely by way ofexample, the network 150 may include a cable network, a wired network,an optical fiber network, a telecommunication network, an internalnetwork, Internet, a local area network (LAN), a wide area network(WAN), a wireless local area network (WLAN), a metropolitan area network(MAN), a wide area network (WAN), a public switched telephone network(PSTN), a Bluetooth network, a Zigbee network, a near fieldcommunication (NFC) network, a global system for mobile communications(GSM) network, a code division multiple access (CDMA) network, a timedivision multiple access (TDMA) network, a general packet radio service(GPRS) network, an enhanced data rate GSM evolution (EDGE) network, awideband code division multiple access (WCDMA) network, a high-speeddownlink packet access (HSDPA) network, a long-term evolution (LTE)network, a user datagram protocol (UDP) network, a transmission controlprotocol/Internet protocol (TCP/IP) network, a short message service(SMS) network, a wireless application protocol (WAP) network, aultra-wideband (UWB) network, an infrared, or the like, or anycombination thereof. In some embodiments, the current page informationrefresh system 100 may include one or more network access points. Forexample, the current page information refresh system 100 may includewired or wireless network access points, such as base stations and/orwireless network access points 150-1, 150-2, . . . , through which oneor more components of the current page information refresh system 100may be connected to the network 150 to exchange data and/or information.

In some embodiments, the information source 140 may generally refer toall information sources except the information provided by the userterminal 120. The information source 140 may include, but is not limitedto, various information sources that may provide information, such as ashopping website, a portal website, a stock exchange, a microblogging, ablog, a personal website, a library, or the like. The information source140 may be implemented in a single central server, multiple servers, ormultiple personal devices connected through a communication link. Whenthe information source 140 is implemented in the multiple personaldevices, the personal device may generate content (e.g., referred to asa “user-generated content”), such as an uploading text, a sound, animage, a video, etc., to the cloud server, thus the cloud server and themultiple personal devices connected to the cloud server may form theinformation source. In some embodiments, the information source 140 mayprovide some relevant information, including but is not limited to,securities news, market analysis, social hotspots, financial opinions,market analysis, industry research reports, company announcements,investment opportunities, funds, commodities, Hong Kong stocks, USstocks, or the like, or any combination thereof.

FIG. 2 is a block diagram of a current page information refresh systemaccording to some embodiments of the present disclosure.

As shown in FIG. 2, the current page information refresh system mayinclude an obtaining module 210, a determination module 220, a displaymodule 230, and a machine learning model training module 240.

The obtaining module 210 may be configured to obtain currently-displayedinformation data in response to receiving an information refresh requestfrom a user. In some embodiments, the information refresh request fromthe user may be made by a coded gesture, a continuous click operation, akey click operation, a touch screen operation in a pause state, acontinuous shaking operation, a voice input operation, a facerecognition, a facial expression recognition, or an iris recognition. Insome embodiments, the information refresh request may be areverse-information refresh request. In some embodiments, theinformation refresh request may include a reversal threshold. Thereversal threshold may be used to characterize a degree of associationbetween refreshed information and the currently-displayed informationdata. In some embodiments, a category of the reversal threshold may atleast include a reversal of a same type, a reversal of different types,or a reversal of a minority type. More descriptions regarding obtainingthe currently-displayed information data may be found in FIG. 3, whichare not repeated here.

The determination module 220 may be configured to determinepossibly-interested information of a user based on information refreshrequest and currently-displayed information data. Specifically, a typeof the currently-displayed information data may be determined based onthe currently-displayed information data. A reversal processingoperation may then be performed on the type of the currently-displayedinformation data based on the information refresh request and the typeof the currently-displayed information data to obtain a type of thepossibly-interested information of the user. The possibly-interestedinformation of the user may further be determined based on the type ofthe possibly-interested information of the user. More descriptionsregarding determining the possibly-interested information of the usermay be found in FIG. 4, which are not repeated here.

In some embodiments, the possibly-interested information of the user maybe determined based on the information refresh request, thecurrently-displayed information data, and historical browsinginformation of the user. Specifically, the historical browsinginformation of the user may be obtained in response to receiving theinformation refresh request from the user. A type of information datathat the user is not interested in may be determined based on thecurrently-displayed information data and the historical browsinginformation of the user. The type of the possibly-interested informationof the user may be obtained by performing, based on the informationrefresh request and the type of information data that the user is notinterested in, a reversal operation on the information data that theuser is not interested in. The possibly-interested information of theuser may be determined based on the type of the possibly-interestedinformation of the user. More descriptions regarding determining thepossibly-interested information of the user may be found in FIG. 5,which are not repeated here.

The display module 230 may be configured to display possibly-interestedinformation of a user. Specifically, at least part of thepossibly-interested information of the user may be displayed on aterminal of the user according to a feed stream. More descriptionsregarding displaying the possibly-interested information of the user maybe found in FIG. 3, which are not repeated here.

The machine learning model training module 240 may be configured totrain a preliminary model to obtain a machine learning model.Specifically, a training sample may be obtained. The training sample mayinclude historical displayed information data and a type of thehistorical displayed information data. The type of the historicaldisplayed information data may be labelled as a type of referenceinformation data. The machine learning model may be obtained by trainingthe preliminary model based on the training sample. More descriptionsregarding training the preliminary model to obtain the machine learningmodel may be found in FIG. 6, which are not repeated here.

It should be understood that the system and the modules shown in FIG. 2may be implemented in various ways. For example, in some embodiments,the system and the modules thereof may be implemented by hardware,software, or a combination of software and hardware. The hardwareportion may be realized by dedicated logic. The software portion may bestored in the memory and executed by an appropriate instructionexecution system, such as a microprocessor or a dedicated designhardware. Those skilled in the art may understand that the above methodsand systems may be implemented using computer-executable instructionsand/or included in processor control codes. For example, such codes maybe provided on a carrier medium such as a disk, CD, or DVD-ROM, aprogrammable memory such as a read-only memory (firmware), or a datacarrier such as an optical or an electronic signal carrier. The systemand the modules thereof described in the present disclosure may not onlybe implemented by a hardware circuit such as a very large-scaleintegrated circuit or a gate array, a semiconductor such as a logicchip, a transistor, etc., or a programmable hardware device such as afield programmable gate array, a programmable logic device, etc. Thesystem and the modules thereof also may be implemented by softwareexecuted by various types of processors. The system and the modulesthereof also may be implemented by a combination of the above hardwarecircuit and software (e.g., firmware).

It should be noted that the above description of the current pageinformation refresh system and the modules thereof are merely providedfor the purposes of illustration, and not intended to limit the scope ofthe present disclosure. It should be understood that for those skilledin the art, after understanding the principle of the system, it may bepossible to arbitrarily combine various modules, or form a subsystem toconnect with other modules without departing from the principle. Forexample, in some embodiments, the obtaining module 210, thedetermination module 220, the display module 230, and the machinelearning model training module 240 may be different modules in thesystem, or a single module which can implement the functions of morethan two modules. As another example, the determination module 220 andthe machine learning model training module 240 may be two modules, or asingle module having a function of determining the possibly-interestedinformation of the user and a function of training a model. As anotherexample, modules may share a storage module, or each module may have itsown storage module. Such variations do not depart from the scope of thepresent disclosure.

FIG. 3 is an exemplary flowchart of a process for refreshing currentpage information according to some embodiments of the presentdisclosure.

In operation 310, in response to receiving an information refreshrequest from a user, currently-displayed information data may beobtained. In some embodiments, operation 310 may be implemented by theobtaining module 210.

In some embodiments, the user may be a user who uses or browses aninterface of a current application (also referred to as a first-party),or a user who uses or browses a third-party application through theinterface of the current application. The third-party application may beother applications relative to the current application. For example, ifTencent Video website is the current application, and a Jingdongshopping advertisement appears on a page of the Tencent Video website,then Jingdong may be a third-party application. The user may jump to aJingdong shopping page by clicking the Jingdong shopping advertisementon the page of the Tencent Video website.

In some embodiments, the information refresh request may include aninstruction of the user to request a re-recommendation of acurrently-browsed resource. For example, when the user browses theinterface of Douyin short video APP for a period of time and hopes theDouyin short video APP recommend some new videos, the user may send arefresh request by pulling down a page. In some embodiments, theinformation refresh request from the user may be made by a codedgesture, a continuous click operation, a key click operation, a touchscreen operation in a pause state, a continuous shaking operation, avoice input operation, a face recognition, a facial expressionrecognition, or an iris recognition. The coded gesture may includedrawing x, drawing √, or drawing a Z-shape on a screen. The codedgesture may also include clicking a code input controller on a screen,and inputting a digital code or a specific character in a pop-up codeinput box. The continuous click operation may include completingmultiple clicks (e.g., 2 times, 3 times, or 4 times) on a screen in ashort period of time (e.g., 3 seconds). The key click operation may bethat, a function key is set on an interface of a current application,and the information refresh request is completed by clicking thefunction key. The touch screen operation in a pause state may includetouching a screen continuously in a period of time (e.g., 3 seconds, 4seconds, or 5 seconds). The continuous shaking operation may includecontinuously (e.g., 3 seconds, 4 seconds, or 5 seconds) keeping a userterminal in a motion state (e.g., shaking a mobile phone strongly for 5seconds) based on a specific intensity. The voice input operation may bethat a voice segment of a user includes a specific voice content text orinstruction, for example, a content text or instruction including“information refresh.” The face recognition may be that an image or avideo stream of a human face is obtained via a camera device on a userterminal, and an identity recognition is performed based on facialfeature information to verify whether to send the information refreshrequest. For example, if the face verification is passed, an instructionof the information refresh request may be sent; otherwise, theinstruction of the information refresh request may not be sent. Thefacial expression recognition may be that, an image or a video stream ofa human face is obtained via a camera of a user terminal, and a specificfacial expression state is separated to verify whether to send theinformation refresh request. For example, if the facial expression stateis “smile,” the verification may be passed, and the information refreshrequest instruction may be sent. If the facial expression state is otherstates (e.g., frowning, crying, angry), the verification may not bepassed, and the information refresh request instruction may not be sent.The iris recognition may be that, an iris of the user may be obtainedvia a camera of a user terminal to verify whether to send theinformation refresh request. For example, if the iris recognition ispassed, the information refresh request instruction may be sent;otherwise, the information refresh request instruction may not be sent.

In some embodiments, the currently-displayed information data may beinformation data of the first-party application currently displayed onthe user terminal, or information data of the third-party applicationcurrently displayed on the user terminal.

The information refresh request may be a request to refresh theinformation currently displayed on the page of the user terminal. Insome embodiments, the information refresh request may be aresemble-information refresh request. The resemble-information refreshrequest may include an instruction of the user to request are-recommendation of other information of the same type as theinformation currently displayed on the page of the user terminal. Insome embodiments, the information refresh request may be areverse-information refresh request. The reverse-information refreshrequest may include an instruction of the user to request are-recommendation of other information of a different type from theinformation currently displayed on the page of the user terminal. Insome embodiments, the way in which the user sends theresemble-information refresh request or the reverse-information refreshrequest may be preset by the user, or may be a default system setting.

In some embodiments, the information refresh request may include areversal threshold. The reversal threshold may be used to characterize adegree of association between refreshed information and thecurrently-displayed information data, that is, a reversal level or areversal degree. In some embodiments, the reversal threshold may include0-10 levels, or other symbols describing the intensity of the levels(e.g., A-K levels), which is not limited in the present disclosure.Specifically, if the reversal threshold is relatively high, the reversaldegree of the recommended content may be relatively high, and the degreeof association between the refreshed information and thecurrently-displayed information data may be relatively low. If thereversal threshold is relatively low, the recommended content may bepartially reversed, and the degree of association between the refreshedinformation and the currently-displayed information data may berelatively high. For example, if a user uses a car APP, and a pricerange of cars that the user has viewed or paid attention for a long timeis between 100,000 and 200,000. When the user sets the reversalthreshold as level 0, after the user sends a reverse-information refreshrequest, the price of the recommended cars of the car APP may be between100,000 and 200,000. When the user sets the reversal threshold as level10, after the user sends the reverse-information refresh request, thecar APP recommends cars with the price other than the price range of100,000 to 200,000. When the user sets the reversal threshold as levels1-9, after the user sends the reverse-information refresh request, thecar APP recommends cars with the price between 100,000 and 200,000, andcars in other price ranges.

In some embodiments, a category of the reversal threshold may at leastinclude a reversal of a same type, a reversal of different types, or areversal of a minority type. The reversal of the same type may be areversal of a subtype in a same major type. For example, a subtype in amajor type may be a basketball in sports, and the reversal of a sametype may be the latest news of the sports such as a table tennis, abadminton, a volleyball, etc. The reversal of different types may be areversal of a major type. For example, if a certain type is sports, andthe reversal of different types may be an entertainment, a military, afinance, a tourism, a history, etc. The reversal of the minority typemay be a type (including the major type and/or the subtype) with lowattention and few attention groups, such as an astronomy, a mathematicalconjecture, a religious study, a curling project, etc. In someembodiments, the category of the reversal threshold may be set as atleast one of the reversal of the same type, the reversal of differenttypes, or the reversal of the minority type. In some embodiments, asetting method of the reversal threshold may include a radar chart, apercentage, a level, or the like. In some embodiments, the radar chartmay include one or more categories of the reversal threshold. Thereversal degree of the reverse refresh may be adjusted by setting theproportions of different categories of the reversal threshold. Forexample, the radar chart may include four categories: a non-reversal ofa same type, the reversal of the same type, the reversal of differenttypes, and the reversal of the minority type. The non-reversal of thesame type may be that the type of the refreshed information is the sameas the type of the currently-displayed information. The proportions ofthe four categories may be set as 0%, 10%, 50%, and 40%. The recommendedcontent may not include the content with the same type as thecurrently-displayed information data, but may include 10% reversalcontent of the same type, 50% reversal of different types, and 40%reversal of the minority type. As another example, the proportion of thenon-reversal of the same type may be set as 0%, the proportion of thereversal of the same type may be set as 0%, the proportion of thereversal of different types may be set as 0%, and the proportion of thereversal of the minority type may be set as 100%. The recommendedcontent may only include the reversal content of the minority type.Optionally, the radar chart may include only three categories: thereversal of the same type, the reversal of different types, and thereversal of the minority type. The radar chart may also be in otherforms, which is not limited in the present disclosure. By setting theproportions of different categories, the recommended content may bereversed in different types and degrees. As another example, the degreeof reverse refresh may be adjusted by setting different percentages orlevels. The percentage or level may correspond to the proportion of eachcategory of different categories of the reversal threshold. Merely byway of example, the percentage of the reversal threshold may be set as80% (or level H), and the recommended content may include 50% reversalof different types and 50% reversal of the minority type. The percentageor level may be preset by the system, or determined by a backgroundserver through a corresponding algorithm, which is not limited in thepresent disclosure. In some embodiments, after the user sends theinformation refresh request, the reversal threshold may be set in afunction setting box that pops up on an application page. The reversalthreshold may also be set through a function controller of theapplication page before the user sends the information refresh request.The reversal threshold may also be a system default setting.

In some embodiments, the reverse-information refresh request from theuser may be the reverse-information refresh request for the recommendedcontent of a current application from the user.

In some embodiments, the reverse-information refresh request from theuser may be the reverse-information refresh request for the recommendedcontent of a third-party application from the user. The third-partyapplication may obtain the reverse-information refresh request for thethird-party application from the user on the current application. Insome embodiments, the third-party application may obtain thereverse-information refresh request for the third-party application fromthe user based on a plug-in of the third-party application. The plug-inof the third-party application may only run on a first-party applicationplatform specified by a program (multiple platforms may be supported atthe same time), and cannot run separately from the designatedfirst-party application platform. For example, Taobao needs to place anadvertisement on an application platform such as Toutiao, Douyin, Weibo,etc., and a plug-in of Taobao for the third-party application maysupport the first-party application platform such as Toutiao, Douyin,Weibo, etc., to enter Taobao for shopping. The plug-in of thethird-party application may be a tool for the third-party application tointeract with the current application. For example, the plug-in of thethird-party application may have functions of obtaining thereverse-information refresh request for the third-party application fromthe user on the current application, obtaining the currently-displayedinformation data of the third-party application, determining thepossibly-interested information of the third-party application of theuser, and displaying the possibly-interested information of thethird-party application to the user, or the like.

In some embodiments, the obtaining module 210 may obtain the informationrefresh request of the user by an operation (e.g., a coded gesture, acontinuous click operation, a key click operation, a touch screenoperation in a pause state, a continuous shaking operation, a voiceinput operation, a face recognition, a facial expression recognition, oran iris recognition) of the user on the user terminal. By setting arelatively simple information refresh request operation, the user canuse the application more conveniently and quickly, which can bring abetter use experience to the user.

In operation 320, possibly-interested information of the user may bedetermined based on the information refresh request and thecurrently-displayed information data. In some embodiments, operation 320may be implemented by the determination module 220.

In some embodiments, the possibly-interested information of the user mayinclude information that the user desires to browse. In someembodiments, the possibly-interested information of the user may beinformation of a first-party application that the user desires tobrowse, or information of a third-party application that the userdesires to browse.

In some embodiments, the possibly-interested information of the user maybe determined based on the information refresh request and thecurrently-displayed information data. Specifically, a type of thecurrently-displayed information data may be determined based on thecurrently-displayed information data. A type of the possibly-interestedinformation of the user may be obtained by performing, based on theinformation refresh request and the type of the currently-displayedinformation data, a reversal operation on the type of thecurrently-displayed information data. The possibly-interestedinformation of the user may be determined based on the type of thepossibly-interested information of the user. More descriptions regardingdetermining the possibly-interested information of the user may be foundin FIG. 4, which are not repeated here.

In some embodiments, the possibly-interested information of the user maybe determined based on the information refresh request, thecurrently-displayed information data, and historical browsinginformation of the user. Specifically, in response to receiving theinformation refresh request from the user, the historical browsinginformation of the user may be obtained. A type of information data thatthe user is not interested in may be determined based on thecurrently-displayed information data and the historical browsinginformation of the user. The type of the possibly-interested informationof the user may be obtained by performing, based on the informationrefresh request and the type of information data that the user is notinterested in, the reversal operation on the type of the informationdata that the user is not interested in. The possibly-interestedinformation of the user may be determined based on the type of thepossibly-interested information of the user. More descriptions regardingdetermining the possibly-interested information of the user may be foundin FIG. 5, which are not repeated here.

In some embodiments, if the information refresh request of the user isan information refresh request for a third-party application, a plug-inof the third-party application may determine the possibly-interestedinformation of the user based on the currently-displayed informationdata of a current application. Specifically, the plug-in of thethird-party application may map a data type of the current application(the first-party application) to a data type of the third-partyapplication, and establish a correspondence relationship between atleast one data type of the current application and at least one datatype of the third-party application. The type of the currently-displayedinformation data of the current application may be determined based onthe currently-displayed information data of the current application andthe data type of the current application (the first-party). Thecorresponding type of the information data of the third-partyapplication may be determined based on the correspondence relationshipbetween data types. The type of the possibly-interested information ofthe user on the third-party application may be determined by performinga reversal operation on the corresponding type of the information dataof the third-party application. The possibly-interested information ofthe user on the third-party application may be determined. Thedetermination of the possibly-interested information of the user on thethird-party application may be similar to the descriptions of FIG. 4,which are not repeated here. Optionally, the correspondence relationshipbetween the at least one data type in the current application and the atleast one data type in the third-party application may be one-to-one,one-to-many, many-to-one, or many-to-many, which is not limited in thepresent disclosure. More descriptions regarding obtaining the data typein the current application (the first-party application) may be found inoperation 420, which are not repeated here.

In some embodiments, if the information refresh request of the user isthe information refresh request for a third-party application, a plug-inof the third-party application may determine the possibly-interestedinformation of the user based on the currently-displayed informationdata of the current application and historical browsing information ofthe user of the current application. Specifically, the plug-in of thethird-party application may map the data type of the current application(the first-party application) to the data type of the third-partyapplication, and establish the correspondence relationship between theat least one data type of the current application and the at least onedata type of the third-party application. A type of information datathat the user is not interested in on the current application may bedetermined based on the currently-displayed information data of thecurrent application, the historical browsing information of the user ofthe current application, and a data type of the current application (thefirst-party). The corresponding type of information data that the useris not interested in on the third-party application may be determinedbased on the correspondence relationship between data types. The type ofthe possibly-interested information of the user on the third-partyapplication may be obtained by performing the reversal operation on thetype of information data that the user is not interested in on thethird-party application. The possibly-interested information of the useron the third-party application may be determined. The descriptions ofthe determination of the possibly-interested information of the user onthe third-party application may be similar to the descriptions of FIG.5, which are not repeated here.

In some embodiments, the determination module 220 may determine thepossibly-interested information of the user based on the informationrefresh request and the currently-displayed information data.

In operation 330, the possibly-interested information of the user may bedisplayed. In some embodiments, operation 330 may be implemented by thedisplay module 230.

In some embodiments, the possibly-interested information of the user maybe all data information generated within a period of time (e.g., oneweek, three days, one day, twelve hours, or one hour) before a currenttime in a current application or a third-party application. In someembodiments, at least part of the possibly-interested information of theuser may be displayed on a page of the user terminal according to a feedstream. The feed stream may be a way of displaying content to the userand updating continuously. The possibly-interested information of theuser may be determined based on the information refresh request and thecurrently-displayed information data. The possibly-interestedinformation of the user may be displayed by refreshing a page. In someembodiments, the feed stream may include three modes including a pushmode, a pull mode, and a push-pull hybrid mode. In some embodiments, thepush mode may be that a server pushes the determined possibly-interestedinformation of the user to some other users after the user generatescontent, which may be suitable for an application with a relativelyuniform number of user relationships and has an upper limit, such as acircle of friends. The pull mode may be that when an information refreshrequest is issued, a page may display updated data according to acertain rule, such as an update time, a popularity, an editor'srecommendation, etc., which may be suitable for an application with asmall number of users and a low daily activity. The push-pull hybridmode may include an online push and an offline pull (e.g., after a Weiboverified person publishes news, the news may be pushed to online fans,and offline fans may pull the news after they are online), and a timingpush and an offline pull (e.g., after a Weibo verified person publishednews, the news may be pushed to the fans in a form of a permanentprocess). In some embodiments, the feed stream may continuously updatethe possibly-interested information of the user based on a useroperation (e.g., a pull-up or pull-down operation). In some embodiments,the possibly-interested information of the user displayed in the feedstream may be sorted by a timeline (a time order), for example, sortedby a chronological order of publication. The content that are releasedfirst may be seen first, and the content that are released later may bearranged at the top. In some embodiments, the possibly-interestedinformation of the user displayed in the feed stream may be sorted by arank (a non-time factor), for example, sorted by a degree of popularity.The possibly-interested information of the user may be sorted by adegree of popularity, and the most popular information may berecommended first.

It should be noted that the above description of the process 300 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be made tothe process 300 under the teachings of the present disclosure. However,those variations and modifications do not depart from the scope of thepresent disclosure. For example, in operation 310, thecurrently-displayed information data may be obtained when the user logsin the current application platform. The possibly-interested informationof the user may be determined when receiving the information refreshrequest of the user. It does not have to be limited to the obtainingafter receiving the information refresh request of the user.

FIG. 4 is an exemplary flowchart of a process for determiningpossibly-interested information of a user according to some embodimentsof the present disclosure. As shown in FIG. 4, a process 400 fordetermining the possibly-interested information of the user may include:

In operation 410, a type of currently-displayed information data may bedetermined based on the currently-displayed information data. In someembodiments, operation 410 may be performed by the determination module220.

In some embodiments, the type of the currently-displayed informationdata may be a data type of the currently-displayed information data. Insome embodiments, the data type may be a data tag of massive data in adatabase of the current page information refresh system (e.g., thestorage device 130). The data tag may be obtained by labelling themassive data based on an algorithm. In some embodiments, the data typemay include a major type label and a subtype label. In some embodiments,the data type may include two or more types. The type of thecurrently-displayed information data may include at least one or moredata types.

In some embodiments, the type of the currently-displayed informationdata may be determined using a machine learning model. Specifically, thecurrently-displayed information data may be inputted into the machinelearning model for processing, and the type of the currently-displayedinformation data may be outputted.

In some embodiments, the machine learning model may include aclassification model, for example, a decision tree, a Bayesclassification, a random forest, a support vector machine, a neuralnetwork, or the like. In some embodiments, the decision tree model mayinclude, but is not limited to, a classification and regression tree(CART), an iterative dichotomiser 3 (ID3), a C4.5 algorithm, a randomforest, a chisquared automatic interaction detection (CHAID), amultivariate adaptive regression splines (MARS), a gradient boostingmachine (GBM), or the like, or any combination thereof. The machinelearning model may be obtained based on a preliminary training model.More descriptions regarding the preliminary model and the trainingprocess may be found in FIG. 6, which are not repeated here.

In some embodiments, the determination module 220 may access the machinelearning model stored in the storage device 130 through the network 150,and determine the type of the currently-displayed information data basedon the currently-displayed information data.

In operation 420, a type of the possibly-interested information of theuser may be obtained by performing, based on the information refreshrequest and the type of the currently-displayed information data, areversal operation on the type of the currently-displayed informationdata. In some embodiments, operation 420 may be performed by thedetermination module 220.

In some embodiments, the type of the possibly-interested information ofthe user may include the data type of the information that the userdesires to browse. In some embodiments, at least one data type may beselected from the category of the reversal threshold as the type of thepossibly-interested information of the user based on the type of thecurrently-displayed information data. Specifically, data types may beobtained, and then at least one data type other than the type of thecurrently-displayed information data (i.e., in the category of areversal threshold) may be selected from the data types as the type ofthe possibly-interested information of the user.

The category of the reversal threshold may at least include a reversalof a same type, a reversal of different types, or a reversal of aminority type. Each category of the reversal threshold may include oneor more data types. In some embodiments, the category of the reversalthreshold and the type of the currently-displayed information data mayconstitute a complete set of data types.

In some embodiments, the data type may be obtained by classifying datalabelled with data tags based on a classification algorithm. Theclassification algorithm, as a supervised machine learning method, mayclassify the labelled data types, and a count of types may be fixed. Insome embodiments, the classification algorithm may include a decisiontree algorithm, a K-nearest neighbor (KNN) algorithm, a Bayes algorithm,a support vector machine algorithm, or the like. In some embodiments,the data type may also be obtained by clustering massive data based on aclustering algorithm. In some embodiments, the clustering algorithm mayalso be used as a pre-processing operation of the classificationalgorithm in a data mining algorithm. The clustering algorithm may be anunsupervised machine learning method that does not require a manuallabeling and a pre-training of the classifier. The types may beautomatically generated during the clustering process and the type datamay be uncertain. Preferably, data with a subtype label may be clusteredinto data with a major type label based on the clustering algorithm. Forexample, a part of the data may have a subtype label such as a football,a UEFA champions league, a star Messi, a Barcelona football club, arecent event, etc. The other part of the data may have a subtype labelsuch as a basketball, an NBA, a star Curry, a golden state warrior, aregular season, etc. The two parts of data may be clustered into thedata with a major type label based on a similarity degree or a deeplearning algorithm, and the major type label may be sports. In someembodiments, the clustering algorithm may include a K-Means clusteringalgorithm, a mean shift clustering algorithm, a density-based clusteringalgorithm (a density-based spatial clustering of applications withnoise, DBSCAN), an expectation-maximization (EM) clustering algorithmwith Gaussian hybrid model (GMM), an agglomerative hierarchicalclustering algorithm, a graph community detection (a graph communitydetection) clustering algorithm.

In some embodiments, the data type may be obtained in real-time or inadvance. The real-time acquisition may be obtained based on aclassification algorithm or a clustering algorithm. In some embodiments,the data type may be obtained based on a factor such as a time, a degreeof popularity, a scenario, a collaborative recommendation, etc. Forexample, the data type of data updated in the past week, the data typeof the latest/hottest data, and the data type of data generated by adata producer that the platform cooperates with may be obtained.

The following may use a specific example to describe the process forobtaining the type of the possibly-interested information of the user.

For example, the data type may include major types M={m1, m2, m3},N={n1, n2, n3}, O={o1, o2, o3}, P={p1, p2, p3}, wherein m1, . . . , p1,etc., may represent a subcategory in the major type. If the type of thecurrently-displayed information data are m1 and n2, the type of thepossibly-interested information of the user may be a combination of oneor more different data types in {m2, m3, n1, n3, O={o1, o2, 03}, P={p1,p2, p3}}. The combination of the one or more different data types mayform a plurality of databases of the type of the possibly-interestedinformation of the user. The database of the type of thepossibly-interested information of the user may include a combination of(C₁₀ ¹×C₁₀ ²×C₁₀ ³×C₁₀ ⁴×C₁₀ ⁵×C₁₀ ⁶×C₁₀ ⁷×C₁₀ ⁸×C₁₀ ⁹×C₁₀ ¹⁰) datatypes corresponding to the type of the possibly-interested informationof the user {m2}, {m3}, . . . , {m2, m3, n1, n3, O={o1, o2, 03}, andP={p1, p2, p3}}.

In operation 430, possibly-interested information of the user may bedetermined based on the type of the possibly-interested information ofthe user. In some embodiments, operation 430 may be performed by thedetermination module 220.

Each information type may have one or more corresponding informationdata. In some embodiments, the possibly-interested information of theuser may be determined based on the type of the possibly-interestedinformation of the user. Taking the above example as the example, thedatabase of the type of the possibly-interested information of the usermay include a combination of (C₁₀ ¹×C₁₀ ²×C₁₀ ³×C₁₀ ⁴×C₁₀ ⁵×C₁₀ ⁶×C₁₀⁷×C₁₀ ⁸×C₁₀ ⁹×C₁₀ ¹⁰) data types corresponding to the type of thepossibly-interested information of the user {m2}, {m3}, . . . , {m2, m3,n1, n3, O={o1, o2, 03}, and P={p1, p2, p3}}. The possibly-interestedinformation of the user may be information data corresponding to thecombination of (C₁₀ ¹×C₁₀ ²×C₁₀ ³×C₁₀ ⁴×C₁₀ ⁵×C₁₀ ⁶×C₁₀ ⁷×C₁₀ ⁸×C₁₀⁹×C₁₀ ¹⁰) data types. Since there are one or more information datacorresponding to the each information type, the amount ofpossibly-interested information of the user may be relatively large,which are not listed here.

It should be noted that the above description of the process 400 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be made tothe process 400 under the teachings of the present disclosure. However,those variations and modifications do not depart from the scope of thepresent disclosure. For example, the data type and the type of thepossibly-interested information of the user in process 400 may not belimited to the number listed, and may also be other numbers. As anotherexample, in operation 420, the type of the possibly-interestedinformation of the user may be obtained by performing a reversaloperation can be replaced with: at least one data type other than thetype of the currently-displayed information data may be selected fromthe complete set of data types as the type of the possibly-interestedinformation of the user.

FIG. 5 is an exemplary flowchart of a process for determiningpossibly-interested information of a user according to some embodimentsof the present disclosure. As shown in FIG. 5, process 500 fordetermining the possibly-interested information of the user may include:

In operation 510, historical browsing information of a user may beobtained in response to receiving an information refresh request fromthe user. In some embodiments, operation 510 may be performed by theobtaining module 210.

In some embodiments, the historical browsing information of the user mayinclude multi-dimensional information such as a browsing content, abrowsing time, a browsing frequency, etc., on a current application thatthe user browses before (e.g., one month, one week, three days, or oneday) a current moment. The browsing information may include a picture, atext, a video, an audio, or the like. In some embodiments, thehistorical browsing information of the user may include, but is notlimited to, a content and/or a time of a post, a follow, a favorite, acomment, a like, or the like.

In some embodiments, the historical browsing information of the user mayinclude cloud-stored historical browsing data or locally stored cookiedata. The cloud-stored historical browsing data may be the historicalbrowsing data of the user stored in a cloud storage (e.g., the storagedevice 130). The locally stored cookie data may include a small textfile stored in a local client terminal (e.g., the user terminal 120).The cookie data may include personal information and the historicalbrowsing data of the user.

In some embodiments, the obtaining module 210 may obtain the historicalbrowsing information of the user from the cloud storage or the localclient terminal of the user in response to receiving the informationrefresh request from the user.

A portrait of the user may be determined, and a data support may also beprovided for various operating projects by analyzing the historicalbrowsing information of the user. For example, if a count of clicks on aspecific product in a certain shop on a shopping website far exceedsthat of other products, the merchants of the shop may be guided toincrease the production or inventory of the specific product.

In operation 520, a type of information data that the user is notinterested in may be determined based on the currently-displayedinformation data and the historical browsing information of the user. Insome embodiments, operation 520 may be performed by the determinationmodule 220.

In some embodiments, the type of the information data that the user isnot interested in may be a type of information data that the user doesnot want to browse. In some embodiments, the type of information datathat the user is not interested in may be determined based on thecurrently-displayed information data and the historical browsinginformation of the user, respectively. The type of information data thatthe user is not interested in may include the type of thecurrently-displayed information data and a type of historical preferenceof the user. More descriptions regarding determining the type of thecurrently-displayed information data based on the currently-displayedinformation data may be found in operation 410, which are not repeatedhere. In some embodiments, the type of historical preference of the usermay be determined based on the historical browsing information of theuser.

In some embodiments, if the historical browsing information of the useris the cloud-stored historical browsing data, the type of historicalpreference of the user may be obtained by processing the cloud-storedhistorical browsing data. Specifically, the type of historicalpreference of the user may be obtained by processing the historicalbrowsing data using a machine learning model. The machine learning modelmay be the same as the machine learning model in operation 410, whichare not repeated here. More descriptions regarding the training processof the machine learning model may be found in FIG. 6, which are notrepeated here.

In some embodiments, if the historical browsing information of the useris the locally stored cookie data, the type attribute of the locallystored cookie data may be extracted as the type of historical preferenceof the user. Specifically, the locally stored cookie data may beobtained. The information refresh request and a data type (also referredto as a type attribute) of the cookie data may be extracted as the typeof historical preference of the user. The locally stored cookie data maybe an encrypted hash code. The server may decrypt the hash code, andread the data type as the type of historical preference of the user. Forexample, the cookie data decrypted by the server is:document.cookie=“userID=828; userName=hulk; class=Basketball,” and thedata type of the cookie data is “basketball,” thus the type ofhistorical preference of the user may be “basketball.”

The type of information data that the user is not interested in may bedetermined based on the type of the currently-displayed information dataand the type of historical preference of the user.

In some embodiments, the determination module 220 may determine the typeof information data that the user is not interested in based on thecurrently-displayed information data and the historical browsinginformation of the user.

In operation 530, a type of possibly-interested information of the usermay be obtained by performing, based on the information refresh requestand the type of information data that the user is not interested in, areversal operation on the type of information data that the user is notinterested in. In some embodiments, operation 530 may be performed bythe determination module 220.

In some embodiments, at least one data type may be selected from acategory of a reversal threshold as the type of the possibly-interestedinformation of the user based on the type of information data that theuser is not interested in. Specifically, data types may be obtained, andat least one data type other than the type of information data that theuser is not interested in (i.e., in the category of the reversalthreshold) may be selected from the data types as the type of thepossibly-interested information of the user. In some embodiments, thecategory of the reversal threshold and the type of information data thatthe user is not interested in may form a complete set of data types. Thereversal operation on the type of information data that the user is notinterested in may be similar to the reversal operation on the type ofthe currently-displayed information data. More descriptions regardingthe reversal operation may be found in operation 420, which are notrepeated here.

In some embodiments, the determination module 220 may obtain the type ofthe possibly-interested information of the user by performing, based onthe information refresh request and the type of information data thatthe user is not interested in, a reversal operation on the type ofinformation data that the user is not interested in.

In operation 540, possibly-interested information of the user may bedetermined based on the type of the possibly-interested information ofthe user. In some embodiments, operation 540 may be performed by thedisplay module 230.

More descriptions regarding operation 540 may be found in operation 430,which are not repeated here.

It should be noted that the above description of the process 500 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be made tothe process 500 under the teachings of the present disclosure. However,those variations and modifications do not depart from the scope of thepresent disclosure. For example, in operation 530, the type of thepossibly-interested information of the user may be obtained byperforming a reversal operation can be replaced with: at least one datatype other than the type of information data that the user is notinterested in may be selected from the complete set of data types as thetype of the possibly-interested information of the user.

FIG. 6 is an exemplary flowchart of a process for training a machinelearning model according to some embodiments of the present disclosure.In some embodiments, process 600 for training the machine learning modelmay be performed by the machine learning model training module 240.

In operation 610, a training sample may be obtained.

In some embodiments, the training sample may include a certain amount ofhistorical displayed information data and types of the historicaldisplayed information data. The training sample may be configured totrain the machine learning model. The historical displayed informationdata may include historical displayed information data on a userterminal. The type of the historical displayed information data may be adata type corresponding to the historical displayed information data.

In some embodiments, operation 610 may also include pre-processing theobtained training sample to make the training sample satisfies atraining requirement. The pre-processing may include a formatconversion, a normalization, an identification, or the like.

In some embodiments, the machine learning model training module 240 mayalso label the obtained training sample. Specifically, the type of thehistorical displayed information data may be labelled as a type ofreference information data. For example, in a certain training sample,it is known that the type of the historical displayed information datais “sports,” then the training sample may be labelled as “sports.” Insome embodiments, the type of information data of the training samplemay be obtained through a questionnaire survey. For example, a certainamount of historical displayed information data may be selected inadvance, and the corresponding types of the information data may beobtained through a manual questionnaire survey. In some embodiments, thelabel process of the training sample may be performed manually or bycomputer programs.

In some embodiments, the training sample may be divided into a trainingset and a verification set. Specifically, the training sample may bedivided based on a certain ratio. For example, a division ratio may be80% for the training set and 20% for the verification set.

In some embodiments, the machine learning model training module 240 mayaccess the information and/or data stored in the storage device 130 viathe network 150 to obtain the training sample. In some embodiments, themachine learning model training module 240 may obtain the trainingsample via an interface. In some embodiments, the machine learning modeltraining module 240 may obtain the training sample in other ways, whichare not limited in the present disclosure.

In operation 620, a machine learning model may be obtained by training apreliminary model based on the training sample.

In some embodiments, the preliminary model may include a classificationmodel, for example, a decision tree, a Bayes classification, a randomforest, a support vector machine, a neural network, or the like. In someembodiments, the decision tree model may include, but is not limited to,a classification and regression tree (CART), an iterative dichotomiser 3(ID3), a C4.5 algorithm, a random forest, a Chi-squared automaticinteraction detection (CHAID), a multivariate adaptive regressionsplines (MARS), a gradient boosting machine (GBM), or the like, or anycombination thereof.

In some embodiments, the training of the preliminary model may includethe following operations. 1) The sample data may be divided into atraining set, a verification set, and a test set. The sample data may bedivided randomly based on a certain ratio. Preferably, the training setmay account for 85%, the verification set may account for 10%, and thetest set may account for 5%. 2) The sample data in the training set maybe inputted into the preliminary model for training. When the trainingprocess satisfies a certain condition, for example, the number oftraining reaches an upper limit of a predefined number of iterations, ora value of a loss function is less than a predetermined value, thetraining process may be terminated to obtain a trained machine learningmodel. 3) The sample data in the verification set may be inputted intothe trained machine learning model for calculation, and an output resultof the type of information data may be obtained. 4) The output result ofthe sample data in the verification set in 3) and an identificationcorresponding to the sample data (e.g., a type of reference informationdata) may be compared to obtain a comparison result. In someembodiments, the comparison result may include that the output resultmatches and does not match the tag identification. The matching mayindicate that a label difference between the output type of informationdata and the type of reference information data is within 2%, otherwise,it may be regarded as non-matching. If the comparison result satisfies averification requirement (it may be set according to an actual need, forexample, it may be set that, after training, the output type ofinformation data for more than 95% of the sample data in theverification matches the tag of the type of reference information data),then proceed to step 5) for testing. Otherwise, it may be regarded asthe requirement is not met (e.g., the accuracy of the output type ofinformation data may be low). Parameters of the trained model may beadjusted, and step 2) may be performed again according to an adjustedmodel. 5) The sample data in the test set may be inputted into thetrained machine learning model for calculation, and an output result maybe obtained. 6) The output result of the sample data in the test set instep 5) and the identification corresponding to the sample data may becompared to determine whether the training result satisfies arequirement (it may be set according to an actual need, e.g., it may beset that, if the output result obtained by the trained model for morethan 98% of the sample data in the test set matches the correspondingtag identification, the training result can be considered to satisfy therequirement, otherwise, it can be considered that the training resultdoes not satisfy the requirement). If the training result does notsatisfy the requirement, the sample data may be re-prepared, or thetraining set, the verification set, and the test set may be re-divided,and the training may be continued until the model test is passed.

Various changes may be made to the operations and implementation methodsdescribed above. For example, the training set, the verification set,and the test set may be divided according to other methods or ratios.Some of the operations may be omitted, and other operations may beadded.

In some embodiments, the currently-displayed information data and thetype of the currently-displayed information data may be used as trainingsample data to train the machine learning model, and the machinelearning model may be iteratively updated. For example, after the typeof the currently-displayed information data is determined, the type ofthe currently-displayed information data may be used as the trainingsample to update the machine learning model. When the user uses acurrent application or a third-party application again, the accuracy ofthe determination of the type of the currently-displayed informationdata may be improved.

In some embodiments, the machine learning model training module 240 mayaccess the information and/or the data stored in the storage device 130via the network 150 to train a preliminary model based on the trainingsample to obtain the machine learning model.

It should be noted that the above description of the process 600 ismerely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be made tothe process 600 under the teachings of the present disclosure. However,those variations and modifications do not depart from the scope of thepresent disclosure. For example, operation 620 in process 600 mayfurther be subdivided into operation 620 for model training, operation630 for model verification, and operation 640 for model testing, orother operations. As another example, the division ratio may be 90% forthe training set, 7% for the verification set, and 3% for the test set.

The possible beneficial effects of the embodiments of the presentdisclosure may include but are not limited to: (1) Thepossibly-interested information of the user may be determined based onthe currently-displayed information data, and data content of adifferent type from the currently-displayed information data may berecommended to the user, which may broaden a browsing horizon of theuser. (2) A simple information refresh request input way may be set on auser terminal, which may simplify an interactive operation between theuser and an interface of the user terminal, and improve user experience.It should be noted that different embodiments may have differentbeneficial effects. In different embodiments, the possible beneficialeffects may be any one or a combination thereof, or any other beneficialeffects that may be obtained.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “module,” “unit,” “component,” “device,” or “system.”Furthermore, aspects of the present disclosure may take the form of acomputer program product embodied in one or more computer readable mediahaving computer readable program code embodied thereon.

A computer storage medium may include a propagated data signal withcomputer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer storage medium maybe any computer readable medium that is not a computer readable storagemedium and that may communicate, propagate, or transport a program foruse by or in connection with an instruction execution system, apparatus,or device. Program code embodied on a computer readable medium may betransmitted using any appropriate medium, including wireless, wireline,optical fiber cable, RF, or the like, or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claim subject matter lie inless than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities or propertiesused to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

1. A method for refreshing current page information, implemented on acomputing device having at least one processor, at least one storagemedium, and a communication platform connected to a network, comprising:in response to receiving, via the network, a reverse-information refreshrequest through a terminal from a user, obtaining, by the at least oneprocessor, currently-displayed information data of the terminal, whereinthe reverse-information refresh request includes an instruction torequest a recommendation of other information of a different type fromthe currently-displayed information data; determining, by the at leastone processor, possibly-interested information of the user based on thereverse-information refresh request and the currently-displayedinformation data; and displaying, by the at least one processor, thepossibly-interested information of the user on an interface of theterminal.
 2. The method of claim 1, wherein the reverse-informationrefresh request from the user is made by a coded gesture, a continuousclick operation, a key click operation, a touch screen operation in apause state, a continuous shaking operation, a voice input operation, aface recognition, a facial expression recognition, or an irisrecognition.
 3. (canceled)
 4. The method of claim 1, wherein thereverse-information refresh request includes a reversal threshold, andthe reversal threshold is used to characterize a degree of associationbetween refreshed information and the currently-displayed informationdata.
 5. The method of claim 4, wherein a category of the reversalthreshold at least includes a reversal of a same type, a reversal ofdifferent types, or a reversal of a minority type.
 6. The method ofclaim 1, wherein the determining, by the at least one processor,possibly-interested information of the user based on thereverse-information refresh request and the currently-displayedinformation data comprises: determining a type of thecurrently-displayed information data based on the currently-displayedinformation data; obtaining a type of the possibly-interestedinformation of the user by performing, based on the reverse-informationrefresh request and the type of the currently-displayed informationdata, a reversal operation on the type of the currently-displayedinformation data; and determining the possibly-interested information ofthe user based on the type of the possibly-interested information of theuser.
 7. The method of claim 6, wherein the determining a type of thecurrently-displayed information data based on the currently-displayedinformation data comprises: determining the type of thecurrently-displayed information data by processing thecurrently-displayed information data using a machine learning model. 8.The method of claim 7, wherein the machine learning model includes aclassification model, and the machine learning model is obtainedaccording to a process including: obtaining a training sample, whereinthe training sample includes historical displayed information data and atype of the historical displayed information data, and the type of thehistorical displayed information data is labelled as a type of referenceinformation data; and obtaining the machine learning model by training apreliminary model based on the training sample.
 9. The method of claim6, wherein the obtaining a type of the possibly-interested informationof the user by performing, based on the reverse-information refreshrequest and the type of the currently-displayed information data, areversal operation on the type of the currently-displayed informationdata comprises: selecting, based on the type of the currently-displayedinformation data, at least one data type from a category of the reversalthreshold as the type of the possibly-interested information of theuser.
 10. The method of claim 1, wherein the determining, by the atleast one processor, possibly-interested information of the user basedon the reverse-information refresh request and the currently-displayedinformation data comprises: in response to receiving thereverse-information refresh request from the user, obtaining historicalbrowsing information of the user; determining a type of information datathat the user is not interested in based on the currently-displayedinformation data and the historical browsing information of the user;obtaining a type of the possibly-interested information of the user byperforming, based on the reverse-information refresh request and thetype of information data that the user is not interested in, a reversaloperation on the type of the information data that the user is notinterested in; and determining the possibly-interested information ofthe user based on the type of the possibly-interested information of theuser.
 11. The method of claim 1, wherein the displaying, by the at leastone processor, the possibly-interested information of the user on aninterface of the terminal comprises: displaying at least part of thepossibly-interested information of the user on the interface of theterminal of the user according to a feed stream.
 12. A system forrefreshing current page information, comprising: at least one storagedevice configured to store computer instructions; at least one processorin communicate with the at least one storage device, wherein when the atleast one processor executes the computer instructions, the at least oneprocessor causes the system to: in response to receiving, via a network,a reverse-information refresh request through a terminal from a user,obtain currently-displayed information data of the terminal, wherein thereverse-information refresh request includes an instruction to request arecommendation of other information of a different type from thecurrently-displayed information data; determine possibly-interestedinformation of the user based on the reverse-information refresh requestand the currently-displayed information data; and display thepossibly-interested information of the user on an interface of theterminal.
 13. (canceled)
 14. The system of claim 12, wherein todetermine possibly-interested information of the user based on thereverse-information refresh request and the currently-displayedinformation data, the at least one processor further causes the systemto: determine a type of the currently-displayed information data basedon the currently-displayed information data; obtain a type of thepossibly-interested information of the user by performing, based on thereverse-information refresh request and the type of thecurrently-displayed information data, a reversal operation on the typeof the currently-displayed information data; and determine thepossibly-interested information of the user based on the type of thepossibly-interested information of the user.
 15. The system of claim 14,wherein to determine a type of the currently-displayed information databased on the currently-displayed information data, the at least oneprocessor further causes the system to: determine the type of thecurrently-displayed information data by processing thecurrently-displayed information data using a machine learning model. 16.The system of claim 14, wherein to obtain a type of thepossibly-interested information of the user by performing, based on thetype of the currently-displayed information data, a reversal operationon the type of the currently-displayed information data, the at leastone processor further causes the system to: select, based on the type ofthe currently-displayed information data, at least one data type from acategory of a reversal threshold as the type of the possibly-interestedinformation of the user.
 17. The system of claim 12, wherein todetermine the possibly-interested information of the user based on thetype of the possibly-interested information of the user, the at leastone processor further causes the system to: in response to receiving thereverse-information refresh request from the user, obtain historicalbrowsing information of the user; determine a type of information datathat the user is not interested in based on the currently-displayedinformation data and the historical browsing information of the user;obtain a type of the possibly-interested information of the user byperforming, based on the reverse-information refresh request and thetype of information data that the user is not interested in, a reversaloperation on the type of information data that the user is notinterested in; and determine the possibly-interested information of theuser based on the type of the possibly-interested information of theuser.
 18. The system of claim 12, wherein to display thepossibly-interested information of the user on an interface of theterminal, the at least one processor further causes the system to:display at least part of the possibly-interested information of the useron the interface of the terminal of the user according to a feed stream.19. A system for refreshing current page information, comprising: anobtaining module configured to obtain currently-displayed informationdata of a terminal in response to receiving, via a network, areverse-information refresh request through the terminal from a user,wherein the reverse-information refresh request includes an instructionto request a recommendation of other information of a different typefrom the currently-displayed information data; a determining moduleconfigured to determine possibly-interested information of the userbased on the reverse-information refresh request and thecurrently-displayed information data; and a display module configured todisplay the possibly-interested information of the user on an interfaceof the terminal.
 20. (canceled)
 21. The system of claim 12, wherein thereverse-information refresh request from the user is made by a codedgesture, a continuous click operation, a key click operation, a touchscreen operation in a pause state, a continuous shaking operation, avoice input operation, a face recognition, a facial expressionrecognition, or an iris recognition.
 22. The system of claim 12, whereinthe reverse-information refresh request includes a reversal threshold,and the reversal threshold is used to characterize a degree ofassociation between refreshed information and the currently-displayedinformation data.
 23. The system of claim 15, wherein the machinelearning model includes a classification model, and the machine learningmodel is obtained according to a process including: obtaining a trainingsample, wherein the training sample includes historical displayedinformation data and a type of the historical displayed informationdata, and the type of the historical displayed information data islabelled as a type of reference information data; and obtaining themachine learning model by training a preliminary model based on thetraining sample.